Journal Article10.1016/J.WATRES.2020.116349
Using convolutional neural network for predicting cyanobacteria concentrations in river water
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TL;DR: This study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images and characterized its performance variations across the studied river reach.
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About: This article is published in Water Research. The article was published on 01 Nov 2020.
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Citations
Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants.
TL;DR: In this article , six different ML algorithms were examined and compared, varying from shallow to deep learning architectures including Seasonal Autoregressive Integrated Moving Average (SARIMAX), Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Long Short-Term Memory (LSTM).
64
Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea.
Quang Viet Ly,Xuan Cuong Nguyen,Ngoc C. Lê,Tien-Dung Truong,T. H. Hoang,Tae Jun Park,Tahir Maqbool,JongCheol Pyo,Kyung Hwa Cho,Kwang-Sik Lee,Jin Hur +10 more
TL;DR: In this paper, the authors applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea.
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A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.
Yongeun Park,Hankyu Lee,Jae-Ki Shin,Kangmin Chon,SungHwan Kim,Kyung Hwa Cho,Jin Hwi Kim,Sang-Soo Baek +7 more
TL;DR: Wang et al. as mentioned in this paper used artificial neural network (ANN) and support vector machine (SVM) models to predict algae alert levels for the early warning of blooms in a freshwater reservoir.
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Assessment and a review of research on surface water quality modeling
Jing Feng Bai,Jian Zhao,Zhenyu Zhang,Ziqiang Tian +3 more
TL;DR: In this paper , a review of surface water quality models was conducted from both a macro and a micro perspective, focusing on the time trends and regional variations of these models' applications using CiteSpace.
55
A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China.
Hongye Cao,Ling Han,Liangzhi Li +2 more
TL;DR: In this article , a CNN-LSTM integrated model was proposed and applied to the prediction of the Cyanobacterial Harmful Algae Blooms (CyanoHABs) area in Taihu Lake.
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References
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
67K
•Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
•Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
- 06 Jul 2015
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.